Operation load control system and method for corn harvester

Abstract

The invention relates to the technical field of agricultural machinery load control, in particular to a corn harvester operation load control system and method, and the system comprises a power extraction module, a speed regulation and control module, a trend analysis module, a parameter optimization module and a load closed-loop module. According to the method, frequency characteristics are extracted in a periodic and segmented mode through header power signals, load dynamic sensing is carried out, advancing speed adjustment is more real-time and prospective, response sensitivity and stability are improved in combination with PID control, equipment stably operates at multiple crop densities and plots, the torque increment of a main shaft is monitored in real time, and wavelet transformation multi-scale analysis is carried out; load fluctuation fine-grained identification is carried out, a roller gap and power output are dynamically adjusted to optimize parameters with a minimum mean square error, frequency and torque characteristic closed-loop comparison is constructed, control self-matching and continuous self-adjustment are carried out, error minimization and trend tracking feedback are fused, the intelligence of the harvester is enhanced, and load sudden change performance fluctuation and energy consumption loss are reduced.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery load control technology, and in particular to a corn harvester operation load control system and method. Background Technology

[0002] The field of agricultural machinery load control technology includes correlation control methods and devices for monitoring, adjusting, and optimizing the load status of agricultural machinery during operation. The core of this technology is load balancing and energy efficiency management of agricultural machinery during operation, avoiding decreased operational efficiency and equipment wear caused by load fluctuations. Agricultural machinery load control systems adjust machinery operating parameters through real-time sensing and response to the power transmission system, operating devices, and environmental conditions, thereby achieving stable operation. Key technologies in this field include operational status detection, load adjustment logic, actuator control, and feedback mechanisms, and are crucial components of the automation and intelligence of modern agricultural equipment.

[0003] One type of corn harvester load control system refers to a load regulation device and method applied during the operation of a corn harvester. It addresses the load variations generated by the harvester under multiple plots, crop densities, and travel speeds. By measuring the operating status parameters of key harvester components, including the header, threshing device, and conveying system, and based on load variation patterns, it automatically adjusts the harvester's travel speed, header height, and threshing drum speed to achieve dynamic load control. The system typically completes the real-time adjustment process by setting thresholds, constructing load regulation logic rules, and combining them with control circuitry.

[0004] Current technologies for load control in agricultural machinery generally rely on static threshold settings and state feedback responses for adjustment, failing to deeply extract dynamic load characteristics and identify trends. Because most control logic is based on setpoints under discrete operating conditions, a mismatch often occurs between the threshold setting and the current load, leading to lag and untimely adjustments in densely harvested fields or during high-speed movement. Even in scenarios with a sudden increase in crop density, static control logic often fails to adjust travel speed and threshing parameters in time, resulting in header blockage or incomplete threshing, increasing the machinery's burden and energy consumption. Feedback paths are mostly linear and unidirectional, lacking trend prediction and multi-parameter linkage mechanisms, resulting in poor operational stability under varying load conditions, impacting both operational efficiency and equipment lifespan. This structural shortcoming of delayed response to load changes has become a key bottleneck restricting the improvement of intelligent operation. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a corn harvester operating load control system and method. The technical solution is as follows: On the one hand, a corn harvester operating load control system is provided, the system comprising: The power extraction module allows the corn harvester to acquire the header power signal, divide the signal into frequency bands using a fast Fourier transform, extract the frequency distribution features, and transmit them to the speed control module. The speed control module determines the current speed based on the frequency distribution characteristics, adjusts the parameters of the PID controller according to different frequency bands, regulates the driving speed, generates the adjusted speed, and transmits it to the trend analysis module. The trend analysis module monitors the real-time torque increment change of the header spindle based on the adjusted speed, analyzes the torque growth or decline trend through wavelet transform decomposition, identifies the torque change trend of the working state in combination with the adjusted speed, and transmits it to the parameter optimization module. The parameter optimization module, based on the adjusted speed and torque change trends, adjusts the harvester's drum clearance and main force output using the least mean square algorithm, constructs the optimal operating parameter state, and transmits it to the load closed-loop module. The load closed-loop module, based on the operational stability of the optimal operating parameter state, compares the frequency distribution characteristics and torque change trend to determine the current operating load state and adjusts the range of multiple parameters in the next control cycle, thereby achieving load self-adjustment closed-loop optimization.

[0006] As a further aspect of the present invention, the frequency distribution characteristics of the cutting table power signal include frequency change amplitude and frequency domain energy distribution; the adjusted speed includes stable speed value and speed change trend; the torque change trend includes torque increment amplitude, torque change direction, and torque fluctuation frequency; the optimal operating parameter state refers to drum clearance parameter, main force output parameter, and operating matching coefficient; and the operating load state includes load stability, parameter adjustment range, and closed-loop control strength.

[0007] As a further aspect of the present invention, the power extraction module includes: The signal acquisition submodule detects the power signal of the corn harvester's header, continuously records voltage fluctuation data, divides the raw signal into sampling ranges at 0.1-second intervals, and generates the raw signal dataset. The spectrum conversion submodule calls the original signal dataset, applies the fast Fourier transform algorithm to perform spectrum conversion on each sample, sets 20-200Hz as the effective frequency band based on the harvester's rated speed and combined with the rated speed harmonic analysis, and divides the time series according to the working cycle to generate a spectrum segment sequence. The feature quantization submodule, based on the spectrum segmentation sequence, statistically analyzes the amplitude distribution of multiple frequency bands, calculates the ratio of the main frequency component to the frequency band energy, marks the frequency bands with a proportion exceeding 60% as feature frequency bands, and generates frequency distribution features that are transmitted to the speed control module.

[0008] As a further aspect of the present invention, the speed control module includes: The frequency analysis submodule calls the frequency distribution characteristics, calculates the main frequency harmonic energy ratio, compares it with the energy distribution characteristics of the frequency band corresponding to the rated speed, normalizes the difference between the current ratio and the standard ratio, and generates a speed deviation coefficient. The control calculation submodule obtains the speed deviation coefficient as the input deviation signal of the PID control model. The proportional, integral and derivative terms are set with coefficients according to the system response time requirements. The speed deviation coefficient is converted into a time-domain adjustment quantity, and the acceleration extreme value and deceleration rate change gradient are output to construct the adjustment parameter set. The PID control model obtains the proportional coefficient by calculating the ratio of the motor torque coefficient to the product of the system target bandwidth and the motor rotational inertia. The integral time constant is adjusted according to the actual system response curve. The differential attenuation coefficient is determined by the system damping ratio and natural frequency. The differential coefficient is composed of the product of the proportional coefficient and the differential time constant divided by the fixed coefficient N. The output submodule is executed to analyze the acceleration extreme values ​​in the set of adjustment parameters. Combined with the real-time power margin data fed back by the motor controller, the speed adjustment amount is calculated within the power range using linear interpolation, and the adjusted speed is generated and transmitted to the trend analysis module.

[0009] As a further aspect of the present invention, the trend analysis module includes: The torque monitoring submodule monitors the current signal of the torque sensor of the header spindle based on the adjusted speed, records the fluctuation difference of the torque value in adjacent sampling periods, calculates the torque change, and generates a real-time torque increment sequence. The trend decomposition submodule calls the real-time torque increment sequence, selects the db4 wavelet basis function that matches the torque signal to perform 5-level decomposition, extracts the fourth-level approximation coefficient as the trend component, calculates the ratio of the trend component variance to the original sequence variance, and calculates the product of the ratio and the velocity correlation factor to generate the trend energy distribution coefficient. State recognition submodule: Obtains the trend energy distribution coefficient, calculates the change in trend coefficient in combination with the adjusted speed, marks an upward trend when the change is continuously positive, marks a downward trend when it is continuously negative, marks a stable state when the change fluctuates to zero, generates the torque change trend and transmits it to the parameter optimization module.

[0010] As a further aspect of the present invention, the change in the trend coefficient of the adjusted speed is calculated using the following formula: ; in, This represents the change in the trend coefficient. The normalization factor represents the trend strength. Representing the Dynamic weighting coefficients for each time window. Representing the Calibrated velocity measurements at each sampling point Represents the reference speed value. Represents the standard deviation of historical velocity fluctuations. Represents the variance stabilization compensation amount. Represents the number of effective time windows. Represents the offset suppression constant. This represents the total number of valid data sampling points.

[0011] As a further aspect of the present invention, the parameter optimization module includes: The parameter association submodule calls the torque change trend and the adjusted speed value, calculates the covariance between the speed change rate and the torque trend identifier, normalizes the main diagonal elements of the covariance, and generates a dynamic coupling coefficient. The algorithm operation submodule, based on the dynamic coupling coefficient, applies the least mean square algorithm to construct an error function, iteratively calculates the relationship between the roller gap adjustment amount and the partial derivative of the crop density collected by the pressure sensor, updates the gap parameters through the gradient descent method, and generates a gap optimization parameter set; The adjustment submodule is executed to obtain the set of clearance optimization parameters, divide the stroke range of the hydraulic actuator into equidistant control nodes, calculate the multi-node opening correction amount according to the optimization parameters, generate the optimal operating parameter state and transmit it to the load closed-loop module.

[0012] As a further aspect of the present invention, the load closed-loop module includes: The parameter monitoring submodule, based on the optimal operating parameter state, collects the output frequency sequence and drive torque sequence of the operating equipment in real time, calculates the standard deviation of the frequency sequence and the difference between the torque sequence, calculates the frequency offset and torque fluctuation with the preset benchmark standard deviation, and constructs an initial parameter set; The feature analysis submodule calls the frequency offset in the initial parameter set, calculates its mean and variance, and simultaneously performs linear regression fitting on the torque fluctuation to obtain the slope. It constructs a three-dimensional feature vector with the mean, variance, and slope, and uses the Mahalanobis distance algorithm to calculate the difference between the current state and the optimal state distribution, and outputs the feature deviation coefficient. The parameter adjustment submodule, based on the characteristic deviation coefficient, back-calculates the speed adjustment amount through the frequency-torque coupling equation, calculates the compensation parameters using the Newton iteration method, writes the speed adjustment step size and torque compensation amplitude into the control register, and performs load self-adjustment closed-loop optimization. The control register includes a parameter storage area, an update control area, and a status feedback area.

[0013] As a further aspect of the present invention, the speed adjustment amount is derived by inversely calculating the frequency-torque coupling equation using the following formula: ; in, It is the speed adjustment amount. This represents the current measured torque value. This represents the rated torque calibration value. Represents the actual frequency sample value. Represents the reference frequency setting value. This represents the transmission efficiency correction factor. Represents the torque smoothing factor. This represents the basic transmission efficiency coefficient. Represents the torque deviation attenuation factor. Represents a nonlinear exponent. This represents the frequency coupling index.

[0014] On the other hand, a method for controlling the operating load of a corn harvester is provided. This method is applied to the operating load control system of a corn harvester and includes: S1: The corn harvester acquires the header power signal, performs periodic segmentation on the signal using fast Fourier transform, and extracts the frequency distribution characteristics; S2: Determine the current speed based on the frequency distribution characteristics, and adjust the driving speed through the PID control model to generate the adjusted speed; S3: Based on the adjusted speed, monitor the real-time torque increment change of the cutting table spindle, analyze the torque growth or decline trend through wavelet transform decomposition, and identify the torque change trend of the working state in combination with the adjusted speed. S4: Based on the adjusted speed and torque change trends, the harvester's drum clearance and main force output are adjusted using the least mean square algorithm to construct the optimal operating parameter state; S5: Based on the operational stability of the optimal operating parameter state, the frequency distribution characteristics and torque change trends are compared to determine the current operating load state and adjust the range of multiple parameters in the next control cycle to achieve load self-adjustment closed-loop optimization.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By periodically segmenting the header power signal and extracting its frequency distribution characteristics, a dynamic sensing capability for the load signal is established, making the adjustment of the travel speed more real-time and forward-looking in response to changes in the working load. Combining a PID control model to adjust the speed improves response sensitivity and stability, thereby enabling the equipment to maintain stable operation under crop density and multiple plot conditions. Real-time monitoring of incremental changes in the header spindle torque and multi-scale trend analysis using wavelet transform allows for fine-grained identification of load fluctuations. Iterative dynamic adjustment of drum clearance and power output optimizes operating parameters with minimum mean square error, ensuring high efficiency in threshing and conveying processes. A closed-loop comparison mechanism is constructed between parameter adjustment results and frequency and torque characteristics, enabling the control process to have self-matching optimization capabilities and continuous self-adjustment of load control. High-frequency dynamic identification is performed at the sensing level, the principle of error minimization is integrated into the adjustment mechanism, and trend tracking and feedback closed loops are introduced into the control flow, making the harvester operation intelligent and efficient, reducing performance fluctuations and energy losses caused by sudden load changes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a system block diagram of the present invention; Figure 3 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] This invention provides a corn harvester operating load control system. Please refer to [link / reference]. Figures 1 to 2 The present invention provides a technical solution: a corn harvester operating load control system includes: The power extraction module allows the corn harvester to acquire the header power signal, divide the signal into frequency bands using a fast Fourier transform, extract the frequency distribution features, and transmit them to the speed control module. The speed control module determines the current speed based on frequency distribution characteristics, adjusts the parameters of the PID controller according to different frequency bands, regulates the driving speed, generates the adjusted speed, and transmits it to the trend analysis module. The trend analysis module monitors the real-time torque increment of the header spindle based on the adjusted speed. It analyzes the torque growth or decline trend through wavelet transform decomposition, identifies the torque change trend of the working state in combination with the adjusted speed, and transmits it to the parameter optimization module. The parameter optimization module, based on the adjusted speed and torque change trends, adjusts the harvester's drum clearance and main force output using the least mean square algorithm to construct the optimal operating parameter state and transmit it to the load closed-loop module. The load closed-loop module, based on the operational stability of the optimal operating parameter state, compares the frequency distribution characteristics and torque change trends to determine the current operating load state and adjusts the range of multiple parameters in the next control cycle, thereby achieving load self-adjustment closed-loop optimization.

[0024] The frequency distribution characteristics of the cutting table power signal include frequency change amplitude and frequency domain energy distribution. The adjusted speed includes stable speed value and speed change trend. The torque change trend includes torque increment amplitude, torque change direction, and torque fluctuation frequency. The optimal operating parameter state refers to the drum clearance parameter, main force output parameter, and operating matching coefficient. The operating load state includes load stability, parameter adjustment range, and closed-loop control strength.

[0025] Please see Figure 2 The power extraction module includes: The signal acquisition submodule detects the power signal of the corn harvester's header, continuously records voltage fluctuation data, divides the raw signal into sampling ranges at 0.1-second intervals, and generates the raw signal dataset. Voltage signals are acquired by a Hall effect sensor (model A1324, range ±5V) installed on the header drive shaft of the corn harvester. The sensor output is connected to a 24-bit ADC data acquisition card (sampling rate 10kHz). During harvester operation, the data acquisition card captures 1000 consecutive sampling points at a period of 0.1 seconds to form a data unit. When the detected voltage value exceeds the normal operating range of 0.3-4.7V, an abnormal flag is triggered. For example, under the condition of a sudden increase in corn density, the sensor measured three consecutive data units with voltage values ​​of 4.8V, 4.9V, and 5.0V exceeding the limit. The system automatically records the timestamp and the magnitude of the exceedance. Table 1 shows a typical data unit collected during operation. Data storage adopts a ring buffer structure with a storage depth of 200MB to ensure continuous recording of 8 hours of operation data.

[0026] Table 1. Cutting table voltage sampling data table Timestamp Voltage value (V) Data Unit Number 09：25：30.100 3.21 D20240523001 09：25：30.200 3.45 D20240523002 09：25：30.300 4.82 D20240523003 As shown in Table 1, data unit D20240523003 recorded an over-limit value of 4.82V. The system marked the unit as an abnormal data segment. During processing, each data unit was verified and a dataset containing 1024 valid sampling points was generated. The verification process discarded data units with a sampling interval deviation exceeding ±0.5ms, including data units with a current sampling interval of 0.0993 seconds and a deviation of 0.7ms from the nominal value.

[0027] The spectrum conversion submodule calls the original signal dataset, applies the Fast Fourier Transform algorithm to perform spectrum conversion on each sample, sets 20-200Hz as the effective frequency band based on the harvester's rated speed and combined with the rated speed harmonic analysis, and divides the time series according to the working cycle to generate a spectrum segment sequence. Data units D20240523001-D20240523003 were called, and a 1024-point FFT transformation was performed on each 0.1-second data unit after applying the Hanning window function. Based on the harvester engine's rated speed of 2200 r / min, the fundamental frequency was calculated to be 36.67 Hz. The 2nd to 5th harmonics (73.34-183.35 Hz) were set as the effective frequency band. The peak frequency of the spectrum calculated for unit D20240523001 appeared at 75 Hz (second harmonic) and 148 Hz (fourth harmonic), with amplitudes of 12.3 dBV and 9.8 dBV, respectively. The operation cycle was divided into time series every 10 seconds, generating 100 continuous spectrum segments. A sudden increase in the 36 Hz component was detected in a typical operation cycle, which was found to correspond to a header blockage condition.

[0028] The feature quantization submodule, based on the spectrum segment sequence, statistically analyzes the amplitude distribution of multiple frequency bands, calculates the ratio of the main frequency component to the frequency band energy, marks the frequency bands with a proportion exceeding 60% as feature frequency bands, and generates frequency distribution features that are transmitted to the speed control module. The system calculates the energy percentage of multiple frequency bands and sets an energy threshold of 60% of the total energy. When the energy percentage of a frequency band exceeds the threshold for three consecutive cycles, it is marked as a characteristic frequency band. The calculation process for the energy value of the 150-160Hz frequency band is as follows: the sum of squared amplitudes of 16 spectral points in the frequency band is obtained as Σ(10^dBV / 10), the total energy is the sum of squared amplitudes of all frequency bands, and the calculated frequency band percentage is 63.7%. The system automatically generates a characteristic frequency band registration table and transmits the energy ratio parameter E-ratio=0.637 to the control module. At the same time, the adjacent frequency bands 140-150Hz (21.3%) and 160-170Hz (15.0%) are recorded as auxiliary parameters.

[0029] Please see Figure 2 The speed control module includes: The frequency analysis submodule calls the frequency distribution characteristics, calculates the ratio of the main frequency harmonic energy, compares it with the energy distribution characteristics of the frequency band corresponding to the rated speed, normalizes the difference between the current ratio and the standard ratio, and generates the speed deviation coefficient. The ratio of the main frequency harmonic energy output by calling the feature quantization submodule Read the standard energy ratio corresponding to the rated speed of 2200 r / min. Calculate energy deviation , This represents the ratio of the main frequency energy to the total energy. The normalized baseline range represents the mean of no-load test data. (Based on the maximum permissible deviation of the data statistics), perform normalization calculation. When dealing with the third harmonic (110Hz), the current energy ratio Calculated , As shown in Table 2, the second harmonic ( The standard energy ratio of 0.35 is compared with the current value of 0.42, and the deviation coefficient is calculated. System settings To regulate the triggering conditions.

[0030] Table 2 Harmonic Energy Characteristic Parameters Harmonic number Standard energy ratio Current energy ratio Deviation coefficient 2 0.35 0.42 0.70 3 0.58 0.62 0.40 4 0.07 0.15 0.80 The control calculation submodule acquires the speed deviation coefficient, which is used as the input deviation signal for the PID control model. The proportional, integral, and derivative terms are set with coefficients according to the system response time requirements. The speed deviation coefficient is converted into a time-domain adjustment quantity, and the acceleration extremum and deceleration rate change gradient are output to construct the adjustment parameter set. The PID control model calculates the ratio of the motor torque coefficient to the system target bandwidth and the motor moment of inertia to obtain the proportional coefficient. The integral time constant is adjusted according to the actual system response curve. The differential attenuation coefficient is determined by the system damping ratio and natural frequency. The differential coefficient is composed of the product of the proportional coefficient and the differential time constant divided by the fixed coefficient N. Set the scaling factor (from the formula) Calculate, where the motor torque System bandwidth Integration time constant (The settling time was optimized from 1.2s to 0.8s through step response tests), differential coefficients (from the formula) Calculate the damping ratio. natural frequency Construct the PID control formula: , substitute the deviation signal ,exist Cumulative value of the integral term within the sampling period Differential term calculation Output: Mapping acceleration extrema (correspond (Time limit) and deceleration rate gradient Calculate the current parameters: .

[0031] The execution output submodule analyzes the acceleration extreme values ​​in the adjustment parameter set, combines the real-time power margin data fed back by the motor controller, uses linear interpolation to calculate the speed adjustment within the power range, generates the adjusted speed, and transmits it to the trend analysis module. Obtaining theoretical acceleration Read the real-time power margin of the motor (56.7% of the rated power of 1500W), establish segmented mapping rules: Calculate the current acceleration: Speed ​​adjustment calculation (current speed) , ): .

[0032] Please see Figure 2 The trend analysis module includes: The torque monitoring submodule monitors the current signal of the torque sensor on the header spindle based on the adjusted speed, records the fluctuation difference of the torque value within adjacent sampling periods, calculates the torque change, and generates a real-time torque increment sequence. The spindle current signal is acquired using a Hall torque sensor (model T40F, range 0-200 N·m, output 4-20 mA), and the sampling period is set. The current value is converted using the formula. ( The torque value is calculated using the current value (in mA), including the measured torque value. hour, The torque values ​​for five consecutive cycles were recorded: 140 N·m, 138 N·m, 142 N·m, 145 N·m, and 141 N·m. The fluctuation difference sequence between adjacent cycles, ΔM = [-2, +4, +3, -4], was calculated. When generating the incremental sequence, the absolute value of the negative values ​​was taken, resulting in [2, 4, 3, 4]. When three consecutive differences exceeded the threshold of 5 N·m, an anomaly marker was triggered. Table 3 shows the torque fluctuation data recorded during the operation.

[0033] Table 3 Torque Fluctuation Monitoring Data Table Timestamp Current value (mA) Torque value (N·m) Fluctuation difference 10:15:30.100 14.8 135 - 10:15:30.200 15.6 145 +10 10:15:30.300 15.1 138.75 -6.25 As shown in Table 3, at 10:15:30.200, a fluctuation difference of +10 N·m occurs, which exceeds the set threshold of 5 N·m. The system marks the data of this period as an abnormal segment. When generating the incremental sequence, the abnormally marked data is removed, and the valid fluctuation value is retained for subsequent processing.

[0034] The trend decomposition submodule calls the real-time torque increment sequence, selects the db4 wavelet basis function that matches the torque signal to perform a 5-level decomposition, extracts the fourth-level approximation coefficient as the trend component, calculates the ratio of the trend component variance to the original sequence variance, and calculates the product of the ratio and the velocity correlation factor to generate the trend energy distribution coefficient. The effective increment sequence [2, 4, 3, 4, 5, 2, 3] is invoked, and a 5-level decomposition is performed using the db4 wavelet to obtain the detail coefficients cD1-cD5 and the approximation coefficients cA5. The fourth-level approximation coefficients cA4 = [0.732, 1.414, 1.035] are extracted, and the variance of the trend component is calculated. (mean) ), original sequence variance Calculate the ratio Speed ​​correlation factor (Based on regression analysis of previous experimental data), generate trend energy distribution coefficients. ,when The existence of a trend component is determined at that time.

[0035] State recognition submodule: Obtains the trend energy distribution coefficient, calculates the change in trend coefficient in combination with the adjusted speed, marks an upward trend when the change is continuously positive, marks a downward trend when it is continuously negative, marks a stable state when the change fluctuates to zero, generates the torque change trend and transmits it to the parameter optimization module; Combined with the adjusted speed Substitute into the formula: ,in, This represents the change in the trend coefficient. The normalization factor represents the trend strength. Representing the Dynamic weighting coefficients for each time window. Representing the Calibrated velocity measurements at each sampling point Represents the reference speed value. Represents the standard deviation of historical velocity fluctuations. Represents the variance stabilization compensation amount. Represents the number of effective time windows. Represents the offset suppression constant. Represents the total number of valid data sampling points. Parameter description: (Trend strength factor, determined by the mean of 50 sets of experimental data). (Three time windows with equal weights) (Base speed) (30-day speed standard deviation) (Compensation amount) (Number of valid windows) (Preventing division by zero constant), when 5 consecutive The time marker indicates an upward trend, and the calculated result 0.048 > 0. Combined with the previous four calculation results [0.041, 0.045, 0.047, 0.048], it is determined to be a continuous upward trend.

[0036] Please see Figure 2 The parameter optimization module includes: The parameter association submodule calls the torque change trend and the adjusted speed value, calculates the covariance between the speed change rate and the torque trend indicator, normalizes the main diagonal elements of the covariance, and generates a dynamic coupling coefficient. The torque trend sequence [rising, rising, stable] and the adjusted speed sequence [1.23, 1.25, 1.24] m / s are used to quantify the trend into numerical values: rising = +1, stable = 0, falling = -1, resulting in [+1, +1, 0]. The rate of change of speed Δu is then calculated as Δu = [(1.25-1.23) / 0.1 = 0.02, (1.24-1.25) / 0.1 = -0.01] m / s. 2 Construct a 3×3 covariance matrix and calculate the elements. , , The maximum value of the main diagonal, 0.00015, is selected, and the normalized baseline value is set to 0.0002 (determined by increasing the maximum covariance by 11% from 0.00018). The dynamic coupling coefficient is then calculated. ,when Strongly coupled tags are generated in real time, and data storage adopts a first-in-first-out queue to keep the most recent 20 sets of data updated.

[0037] The algorithm operation submodule, based on the dynamic coupling coefficient, uses the least mean square algorithm to construct an error function, iteratively calculates the relationship between the roller gap adjustment and the partial derivative of the crop density collected by the pressure sensor, updates the gap parameters through the gradient descent method, and generates a gap optimization parameter set; Algorithm operation submodule receives Construct the error function (crop density) (Measured by PS210 pressure sensor), initializing the roller clearance. Set the learning rate (The optimal value was determined through 10 preliminary experiments), and the gradient was calculated. Execute iteration: Step 1 Step 2 The optimization process terminates when the error change is less than 0.1 mm for three consecutive iterations, generating the parameter set [13.488 mm, 0.75, 0.8 kg / m]. 3 The data is updated 5 times per second.

[0038] The adjustment submodule is executed to obtain the clearance optimization parameter set, divide the hydraulic actuator stroke range into equidistant control nodes, calculate the multi-node opening correction amount according to the optimization parameters, generate the optimal operating parameter state and transmit it to the load closed-loop module. The optimized clearance of 13.488mm was obtained. The 0-50mm stroke of the hydraulic actuator was divided into 10 control nodes (5mm spacing). The node correction was calculated: the correction for the center node 3 (15mm) = 13.488 - 15 = -1.512mm. This correction was then distributed to adjacent nodes using a distance-weighted method. The correction for node 2 = -1.512 × 0.5 = -0.756mm, and the correction for node 4 = -1.512 × 0.5 = -0.756mm. A control parameter table was then generated. Table 4. Hydraulic Actuator Node Adjustment Parameters Node number Original position Distance weight Correction amount New location Allowable error 2 10.0 0.5 -0.756 9.244 ±0.2 3 15.0 1.0 -1.512 13.488 ±0.1 4 20.0 0.5 -0.756 19.244 ±0.2 5 25.0 0.3 -0.454 24.546 ±0.3 As shown in Table 4, the formula for calculating the correction amount is: ( (Number of nodes), the allowable error of node 3 is ±0.1mm, which requires precise control. The data is sent to the actuator via CAN bus at a frequency of 100Hz. The position feedback sensor has a resolution of 0.01mm to ensure that the adjustment accuracy meets the standard.

[0039] Please see Figure 2 The load closed-loop module includes: The parameter monitoring submodule, based on the optimal operating parameter state, collects the output frequency sequence and drive torque sequence of the operating equipment in real time, calculates the standard deviation of the frequency sequence and the difference between the torque sequence, calculates the frequency offset and torque fluctuation with the preset benchmark standard deviation, and constructs the initial parameter set; The frequency sequence [49.8, 50.1, 49.9, 50.2, 49.7] Hz output by the operating equipment was collected using a FQ-210 frequency sensor (accuracy ±0.1Hz), and the standard deviation was calculated. Hz, synchronously acquire the drive torque sequence [148, 152, 145, 155, 150] N·m, calculate the adjacent sampling difference sequence [+4, -7, +10, -5], and set the baseline standard deviation. Hz (determined based on the 90th percentile of 30-day data), calculate the frequency offset. Torque fluctuation N·m, construct the initial parameter set [0.187, 6.5, 24.67%], the data update cycle is 0.2 seconds, and the outlier filtering adopts the 3σ principle, automatically removing data exceeding ±3×0.15=±0.45Hz.

[0040] Table 5 Frequency Torque Monitoring Data Table Timestamp Frequency value (Hz) Torque value (N·m) Wave marker 10：30：15.200 49.8 148 normal 10：30：15.400 50.1 152 normal 10：30：15.600 50.5 160 abnormal As shown in Table 5, at 10:30:15.600, the frequency value of 50.5Hz exceeds the reference range of 49.55-50.45Hz. The system automatically marks it as abnormal data and excludes it from the calculation. The effective data window keeps the most recent 20 sets of effective values ​​and updates them continuously.

[0041] The feature analysis submodule calls the frequency offset in the initial parameter set, calculates its mean and variance, and simultaneously performs linear regression fitting on the torque fluctuation to obtain the slope. It constructs a three-dimensional feature vector with the mean, variance, and slope, and uses the Mahalanobis distance algorithm to calculate the difference between the current state and the optimal state distribution, and outputs the feature deviation coefficient. Using the initial parameter set [0.187, 6.5, 24.67%], perform an arithmetic mean operation to obtain the average frequency offset. variance Linear regression was performed on the torque fluctuation sequence [4, 7, 10, 5], with the time variable t = [1, 2, 3, 4]. The slope was calculated. N·m / s, after obtaining the regression slope by calculating the ratio of the covariance numerator to the variance denominator, construct a three-dimensional feature vector [24.67, 0, 1.4] and an optimal state reference vector [15, 0.5, 0.8]. Calculate the covariance matrix using previous data samples. Perform matrix inversion, calculate the difference vector between the current eigenvector and the optimal reference vector, perform Mahalanobis distance matrix multiplication, and take the square root of the result to calculate the Mahalanobis distance. The deviation coefficient is calculated as the ratio of the Mahalanobis distance value to the baseline value; the characteristic deviation coefficient is... (The baseline value of 8.0 was determined using the 90th percentile of 50 sets of experimental data.) Parameter adjustment is triggered at any time.

[0042] The parameter adjustment submodule, based on the characteristic deviation coefficient, back-calculates the speed adjustment amount through the frequency-torque coupling equation, calculates the compensation parameters using the Newton-Raphson iteration method, writes the speed adjustment step size and torque compensation amplitude into the control register, and performs load self-adjustment closed-loop optimization. The characteristic deviation coefficient stored at receive register address 0x3E8 Substitute the values ​​into the formula to calculate the speed adjustment: ,in, It is the speed adjustment amount. This represents the current measured torque value. This represents the rated torque calibration value. Represents the actual frequency sample value. Represents the reference frequency setting value. This represents the transmission efficiency correction factor. Represents the torque smoothing factor. This represents the basic transmission efficiency coefficient. Represents the torque deviation attenuation factor. Represents a nonlinear exponent. Represents the frequency coupling index; parameter assignment: N·m (current torque) N·m (rated value) Hz, Hz, (Transmission test measured value) (Data regression coefficients) (Nonlinear parameters) (Frequency Coupling Index) N·m (smoothing factor), the compensation parameter is solved using Newton's iteration method, with an initial guess value. ,calculate After 3 iterations, it converges to The speed adjustment step size of 0.0239 r / min and the torque compensation amplitude of 0.736 N·m (=0.0239×30.8, transmission ratio coefficient) are written to the control register address 0x3A5-0x3A8. The status feedback area update flag bit 0x01 indicates that the parameters are effective, and the load self-adjustment closed-loop optimization is completed.

[0043] Please see Figure 3 The methods include: S1: The corn harvester acquires the header power signal, performs periodic segmentation on the signal using fast Fourier transform, and extracts the frequency distribution characteristics; S2: Determine the current speed based on frequency distribution characteristics, and adjust the driving speed through a PID control model to generate the adjusted speed; S3: Based on the adjusted speed, monitor the real-time torque increment change of the header spindle, analyze the torque growth or decline trend through wavelet transform decomposition, and identify the torque change trend of the working state in combination with the adjusted speed. S4: Based on the adjusted speed and torque change trends, the minimum mean square algorithm is used to adjust the harvester's drum clearance and main force output to construct the optimal operating parameter state; S5: Based on the operational stability of the optimal operating parameter state, the frequency distribution characteristics and torque change trends are compared to determine the current operating load state and adjust the range of multiple parameters in the next control cycle to achieve load self-adjustment closed-loop optimization.

[0044] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0045] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0046] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0047] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0048] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0049] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0050] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0052] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A corn harvester operating load control system, characterized in that, The system includes: The power extraction module allows the corn harvester to acquire the header power signal, divide the signal into frequency bands using a fast Fourier transform, extract the frequency distribution features, and transmit them to the speed control module. The speed control module determines the current speed based on the frequency distribution characteristics, adjusts the parameters of the PID controller according to different frequency bands, regulates the driving speed, generates the adjusted speed, and transmits it to the trend analysis module. The trend analysis module monitors the real-time torque increment change of the header spindle based on the adjusted speed, analyzes the torque growth or decline trend through wavelet transform decomposition, identifies the torque change trend of the working state in combination with the adjusted speed, and transmits it to the parameter optimization module. The parameter optimization module, based on the adjusted speed and torque change trends, adjusts the harvester's drum clearance and main force output using the least mean square algorithm, constructs the optimal operating parameter state, and transmits it to the load closed-loop module. The load closed-loop module, based on the operational stability of the optimal operating parameter state, compares the frequency distribution characteristics and torque change trend to determine the current operating load state and adjusts the range of multiple parameters in the next control cycle, thereby achieving load self-adjustment closed-loop optimization.

2. The corn harvester operating load control system according to claim 1, characterized in that, The frequency distribution characteristics of the cutting table power signal include frequency change amplitude and frequency domain energy distribution; the adjusted speed includes stable speed value and speed change trend; the torque change trend includes torque increment amplitude, torque change direction, and torque fluctuation frequency; the optimal operating parameter state refers to drum clearance parameter, main force output parameter, and operating matching coefficient; and the operating load state includes load stability, parameter adjustment range, and closed-loop control strength.

3. The corn harvester load control system according to claim 1, characterized in that, The power extraction module includes: The signal acquisition submodule detects the power signal of the corn harvester's header, continuously records voltage fluctuation data, divides the raw signal into sampling ranges at 0.1-second intervals, and generates the raw signal dataset. The spectrum conversion submodule calls the original signal dataset, applies the fast Fourier transform algorithm to perform spectrum conversion on each sample, sets 20-200Hz as the effective frequency band based on the harvester's rated speed and combined with the rated speed harmonic analysis, and divides the time series according to the working cycle to generate a spectrum segment sequence. The feature quantization submodule, based on the spectrum segmentation sequence, statistically analyzes the amplitude distribution of multiple frequency bands, calculates the ratio of the main frequency component to the frequency band energy, marks the frequency bands with a proportion exceeding 60% as feature frequency bands, and generates frequency distribution features that are transmitted to the speed control module.

4. The corn harvester load control system according to claim 1, characterized in that, The speed control module includes: The frequency analysis submodule calls the frequency distribution characteristics, calculates the main frequency harmonic energy ratio, compares it with the energy distribution characteristics of the frequency band corresponding to the rated speed, normalizes the difference between the current ratio and the standard ratio, and generates a speed deviation coefficient. The control calculation submodule obtains the speed deviation coefficient as the input deviation signal of the PID control model. The proportional, integral and derivative terms are set with coefficients according to the system response time requirements. The speed deviation coefficient is converted into a time-domain adjustment quantity, and the acceleration extreme value and deceleration rate change gradient are output to construct the adjustment parameter set. The PID control model obtains the proportional coefficient by calculating the ratio of the motor torque coefficient to the product of the system target bandwidth and the motor rotational inertia. The integral time constant is adjusted according to the actual system response curve. The differential attenuation coefficient is determined by the system damping ratio and natural frequency. The differential coefficient is composed of the product of the proportional coefficient and the differential time constant divided by the fixed coefficient N. The output submodule is executed to analyze the acceleration extreme values ​​in the set of adjustment parameters. Combined with the real-time power margin data fed back by the motor controller, the speed adjustment amount is calculated within the power range using linear interpolation, and the adjusted speed is generated and transmitted to the trend analysis module.

5. The corn harvester operating load control system according to claim 1, characterized in that, The trend analysis module includes: The torque monitoring submodule monitors the current signal of the torque sensor of the header spindle based on the adjusted speed, records the fluctuation difference of the torque value in adjacent sampling periods, calculates the torque change, and generates a real-time torque increment sequence. The trend decomposition submodule calls the real-time torque increment sequence, selects the db4 wavelet basis function that matches the torque signal to perform 5-level decomposition, extracts the fourth-level approximation coefficient as the trend component, calculates the ratio of the trend component variance to the original sequence variance, and calculates the product of the ratio and the velocity correlation factor to generate the trend energy distribution coefficient. State recognition submodule: Obtains the trend energy distribution coefficient, calculates the change in trend coefficient in combination with the adjusted speed, marks an upward trend when the change is continuously positive, marks a downward trend when it is continuously negative, marks a stable state when the change fluctuates to zero, generates the torque change trend and transmits it to the parameter optimization module.

6. The corn harvester load control system according to claim 5, characterized in that, The change in the trend coefficient of the adjusted speed is calculated using the following formula: ； in, This represents the change in the trend coefficient. The normalization factor represents the trend strength. Representing the Dynamic weighting coefficients for each time window. Representing the Calibrated velocity measurements at each sampling point Represents the reference speed value. Represents the standard deviation of historical velocity fluctuations. Represents the variance stabilization compensation amount. Represents the number of effective time windows. Represents the offset suppression constant. This represents the total number of valid data sampling points.

7. The corn harvester operating load control system according to claim 1, characterized in that, The parameter optimization module includes: The parameter association submodule calls the torque change trend and the adjusted speed value, calculates the covariance between the speed change rate and the torque trend identifier, normalizes the main diagonal elements of the covariance, and generates a dynamic coupling coefficient. The algorithm operation submodule, based on the dynamic coupling coefficient, applies the least mean square algorithm to construct an error function, iteratively calculates the relationship between the roller gap adjustment amount and the partial derivative of the crop density collected by the pressure sensor, updates the gap parameters through the gradient descent method, and generates a gap optimization parameter set; The adjustment submodule is executed to obtain the set of clearance optimization parameters, divide the stroke range of the hydraulic actuator into equidistant control nodes, calculate the multi-node opening correction amount according to the optimization parameters, generate the optimal operating parameter state and transmit it to the load closed-loop module.

8. The corn harvester load control system according to claim 1, characterized in that, The load closed-loop module includes: The parameter monitoring submodule, based on the optimal operating parameter state, collects the output frequency sequence and drive torque sequence of the operating equipment in real time, calculates the standard deviation of the frequency sequence and the difference between the torque sequence, calculates the frequency offset and torque fluctuation with the preset benchmark standard deviation, and constructs an initial parameter set; The feature analysis submodule calls the frequency offset in the initial parameter set, calculates its mean and variance, and simultaneously performs linear regression fitting on the torque fluctuation to obtain the slope. It constructs a three-dimensional feature vector with the mean, variance, and slope, and uses the Mahalanobis distance algorithm to calculate the difference between the current state and the optimal state distribution, and outputs the feature deviation coefficient. The parameter adjustment submodule, based on the characteristic deviation coefficient, back-calculates the speed adjustment amount through the frequency-torque coupling equation, calculates the compensation parameters using the Newton iteration method, writes the speed adjustment step size and torque compensation amplitude into the control register, and performs load self-adjustment closed-loop optimization. The control register includes a parameter storage area, an update control area, and a status feedback area.

9. The corn harvester load control system according to claim 8, characterized in that, The speed adjustment amount is calculated by inversely using the frequency-torque coupling equation, using the following formula: ； in, It is the speed adjustment amount. This represents the current measured torque value. This represents the rated torque calibration value. Represents the actual frequency sample value. Represents the reference frequency setting value. This represents the transmission efficiency correction factor. Represents the torque smoothing factor. This represents the basic transmission efficiency coefficient. Represents the torque deviation attenuation factor. Represents a nonlinear exponent. This represents the frequency coupling index.

10. A method for controlling the operating load of a corn harvester, characterized in that, The method is used to implement the corn harvester operation load control system according to any one of claims 1-9, the method comprising: S1: The corn harvester acquires the header power signal, performs periodic segmentation on the signal using fast Fourier transform, and extracts the frequency distribution characteristics; S2: Determine the current speed based on the frequency distribution characteristics, and adjust the driving speed through the PID control model to generate the adjusted speed; S3: Based on the adjusted speed, monitor the real-time torque increment change of the cutting table spindle, analyze the torque growth or decline trend through wavelet transform decomposition, and identify the torque change trend of the working state in combination with the adjusted speed. S4: Based on the adjusted speed and torque change trends, the roller clearance and main force output of the harvester are adjusted using the least mean square algorithm to construct the optimal operating parameter state; S5: Based on the operational stability of the optimal operating parameter state, compare the frequency distribution characteristics and torque change trend, determine the current operating load state, and adjust the range of multiple parameters in the next control cycle to achieve load self-adjustment closed-loop optimization.

Images